Reply on RC2

Abstract

The chemical composition of ambient organic aerosols plays a critical role in driving their climate and health relevant properties and holds important clues to the sources and formation mechanisms of secondary aerosol material. In most ambient atmospheric environments, this composition remains incompletely characterized, with the number of identifiable species consistently outnumbered by those that have no mass spectral matches in the literature or NIST/NIH/EPA mass spectral databases, making them nearly impossible to definitively identify. This creates significant challenges in utilizing the full analytical capabilities of techniques which separate and generate spectra for complex environmental samples. In this work, we develop the use of machine learning techniques to quantify and characterize novel, or unidentifiable, organic material. This work introduces Ch3MS-RF (Chemical Characterization by Chromatography-Mass Spec Random Forest Modelling), an open-source R-based software tool for efficient machine-learning enabled characterization of compounds separated in chromatography-mass spec applications but not identifiable by comparison to mass spectral databases. A random forest model is trained and tested on a known 130 component representative external standard to predict the response factors of novel environmental organics based on position in volatility-polarity space and mass spectrum, enabling reproducible, efficient, and optimized quantification of novel environmental species. Quantification accuracy on a reserved 20 % test set randomly split from the external standard compound list indicate that random forest modelling significantly outperforms the commonly used methods in both precision and accuracy, with a median response factor % error of -2 % for modelled response factors compared to > 15 % for typically used proxy assignment-based methods. Chemical properties modelling, evaluated on the same reserved 20 % test set as well as an extrapolation set of species identified in ambient organic aerosol samples collected in the amazon rainforest, also demonstrates robust performance. Extrapolation set property prediction mean average errors for carbon number, oxygen to carbon ratio (O : C), average carbon oxidation state , and vapor pressure are 1.8, 0.15, 0.25, and 1.0 (log(atm)), respectively. Extrapolation set Out-of-Sample R2 for all properties modelled are above 0.75, with the exception of vapor pressure. While predictive performance for vapor pressure is less robust compared to the other chemical properties modelled, random forest-based modelling was significantly more accurate than other commonly used methods of vapor pressure prediction, decreasing mean average vapor pressure prediction error to 0.24 (log(atm)) from 0.55 (log(atm)) (chromatography-based vapor pressure prediction) and 1.2 (log(atm)) (chemical formula-based vapor pressure prediction). The random forest model significantly advances untargeted analysis of the full scope of chemical speciation yielded by GCxGC-MS techniques and can be applied to GC-MS as well. It enables accurate estimation of key chemical properties commonly utilized in the atmospheric chemistry community, which may be used to more efficiently identify important tracers for further individual analysis and to characterize compound populations uniquely formed under specific ambient conditions.